Fast features for face authentication under illumination direction changes
Pattern Recognition Letters
On the Use of SIFT Features for Face Authentication
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Taking the bite out of automated naming of characters in TV video
Image and Vision Computing
Visual language model for face clustering in consumer photos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Face-and-clothing based people clustering in video content
Proceedings of the international conference on Multimedia information retrieval
Spatio-temporal tube kernel for actor retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
A GMM parts based face representation for improved verification through relevance adaptation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
User authentication via adapted statistical models of face images
IEEE Transactions on Signal Processing
“Knock! Knock! Who is it?” probabilistic person identification in TV-series
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Unsupervised metric learning for face identification in TV video
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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This paper addresses face diarization in videos, that is, deciding which face appears and when in the video. To achieve this face-track clustering task, we propose a hierarchical approach combining the strength of two complementary measures: (i) a pairwise matching similarity relying on local interest points allowing the accurate clustering of faces tracks captured in similar conditions, a situation typically found in temporally close shots of broadcast videos or in talk-shows; (ii) a biometric cross-likelihood ratio similarity measure relying on Gaussian Mixture Models (GMMs) modeling the distribution of densely sampled local features (Discrete Cosine Transform (DCT) coefficients), that better handle appearance variability. Experiments carried out on a public video dataset and on the data from the French REPERE challenge demonstrate the effectiveness of our approach in comparison with state-of-the-art methods.